**4. Case Study**

Solar power in Vietnam belongs to the emerging energy industry group, which is engaged in the development of the world's renewable energy sources, the import of science and technology, and meets the demand for developing power sources. When large hydropower sources are fully exploited, small hydroelectricity will not guarantee benefits compared to the environmental damage. Vietnam, on the other hand, has grea<sup>t</sup> potential for solar and wind power, due to its proximity to the equator and the existence of dry, sunny regions like the southern central provinces. The study results are shown in Figure 6. Therefore, solar power, together with wind power, is being encouraged to develop by the State of Vietnam, reflected in the Prime Minister's Decision No. 2068/QD-TTg of 25 November 2015, approving the Renewable Energy Development Strategy. Vietnam's electricity generation until 2030, with a vision of 2050, ensures the development of electricity sources when stopping nuclear power projects and reducing fossil-fired thermal power plants.

**Figure 6.** Irradiation Map of Mekong Delta, Vietnam [27].

With the growth of economic sectors in general and agriculture, the demand for electricity in the Mekong Delta provinces is increasing. However, fossil fuel for electricity generation is declining, so the development of renewable energy is one of the sustainable solutions for the Mekong Delta to have electricity to ensure domestic living and economic development.

In this research, the authors present an integrated approach for the site of solar plants in Mekong Delta, Vietnam, by using data envelopment analysis (DEA) and Fuzzy Analytical Network Process (FANP). In the first stage, DEA model is applied to select some potential location, then FANP model is used for ranking these potential locations. The authors collected data from thirteen locations in Mekong Delta, Vietnam, which can invest in solar power plants, as shown in Table 3.


**Table 3.** List of the 13 locations identified in Mekong Delta, Viet Nam.

DEA model is a quantitative technique that determines the relative effectiveness of multiple inputs and outputs decision makers. Halasah et al. [28] employed life-cycle assessment to evaluate the energy-related impacts of PV systems at different scales of integration. The input parameters included panel efficiency, temperature coefficient, shading losses, ground cover ratio and latitude, and the input data included hourly solar radiation, wind speed and temperature. Wang and Amy [9,14] using DEA model for ranking potential location for building solar power plant. In their research, the output data included sunshine hour and elevation. Due to the information accessibility of various sites and the importance of various factors, we select two inputs and two outputs for the quantitative factors. The two inputs are temperature (I1) and wind speed (I2). The two outputs are sunshine hours (O1) and elevation (O2) [14,20]. The definition of the inputs and outputs are defined in Table 4 [20]. Raw data of inputs and outputs of DMUs are demonstrated in Figure 7.



intensity is projected to be higher. Higher intensity yields higher solar energy

output. Panjwani and Narejo discussed how elevation generated a 7–12%

increase in power by testing 3 solar panels at a 27.432 m elevation [32].

**Figure 7.** Inputs and outputs of DEA models.

For applied DEA model, some additional data about 13 locations in Mekong Delta, Vietnam are show in Table 5.


**Table 5.** Raw data of DEA model.

To select some potential sites in Mekong Delta, Vietnam, there are several DEA models, including SBM-I-C; CCR-I; BCC-O; CCR-O and BCC-I, applied in this step. The results of the DEA models are shown in Table 6.

**Table 6.** Ranking results from some DEA models.


As the results in Table 6 show that there are seven DMUs that are potential locations for building a solar power plant in Mekong Delta, Vietnam including DEL01, DEL03, DEL05, DEL09 and DEL11. These DMUs will be evaluated in the next step of this research by using the FANP model.

In the final stage, all the potential locations will be ranked by the FANP model. Some criteria affecting the location selection are shown in Figure 8.

**Figure 8.** Main criteria and sub-criteria in Fuzzy Analytical Network Process (FANP) model.

Fuzzy comparison matrix of EC from the FANP model is shown in Table 7.



The fuzzy numbers were converted to real numbers by using the TFN. During the defuzzification, the authors obtain the coefficients α = 0.5 and β = 0.5. In this, α represents the uncertain environment conditions, and β represents whether the attitude of the evaluator is fair.

$$\text{(g}\_{0.5, 0.5}(\overline{a\_{EL, SL}}) \text{ = [(0.5 \times 4.5) + (1 - 5.5) \times 2.5] = 5.5]}$$

$$\text{f}\_{0.5}(\text{L}\_{\text{EL, SL}}) = (5 - 4) \times 0.5 + 4 = 4.5$$

$$\text{f}\_{0.5}(\text{U}\_{\text{SL, SL}}) = 6 - (6 - 5) \times 0.5 = 5.5$$

$$\mathfrak{g}\_{0.5\backslash 0.5}(\overline{a\_{SL,EL}}) \, = 1/5$$

The remaining calculations for other criteria are similar to the above calculation. The real number priority when comparing the main criteria pairs is shown in Table 8.

**Table 8.** Real number priority.


The following are used for calculating the maximum individual value:

> P1 = (1 × 6 × 2 × 4)<sup>1</sup>/<sup>4</sup> = 2.63 P2 = (1/5 × 1 × 1/4 × 3)<sup>1</sup>/<sup>4</sup> = 0.62 P3 = (1/2 × 4 × 1 × 5)<sup>1</sup>/<sup>4</sup> = 1.78 P4 = (1/4 × 1/3 × 1/5 × 1)<sup>1</sup>/<sup>4</sup> = 0.36 *Q* = 5.39 ω1 = 2.63 5.39 = 0.49 ω2 = 0.62 5.39 = 0.12 ω3 = 1.78 5.39 = 0.33 ω4 = 0.36 5.39 = 0.07 ⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣1 6 24 1/6 1 1/4 3 1/2 4 1 5 1/4 1/3 1/5 1 ⎤ ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ × ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 0.49 0.12 0.33 0.07 ⎤ ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ = ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 2.15 0.49 1.41 0.30 ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 2.15 0.49 1.41 0.30 ⎤ ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ / ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 0.49 0.12 0.33 0.07 ⎤ ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ = ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 4.39 4.08 4.27 4.29 ⎤ ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⎤

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

Based on the number of main criteria, the authors ge<sup>t</sup> n = 5; λmax and CI are calculated as follows:

$$
\lambda\_{\text{max}} = \frac{4.39 + 4.08 + 4.27 + 4.29}{4} = 4.23
$$

$$
CI = \frac{\lambda\_{\text{max}} - n}{n - 1} = \frac{4.23 - 4}{4 - 1} = 0.077
$$

$$
\lambda\_{\text{max}} = \frac{1}{\cdot}
$$

To calculate CR value, we ge<sup>t</sup> RI = 0.9 with n = 4.

$$CR = \frac{CI}{RI} = \frac{0.077}{0.9} = 0.08556$$

As CR = 0.08556 ≤ 0.1, we do not need to re-evaluate.
